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. 2015 Jan 28;10:9–19. doi: 10.4137/BMI.S20089

Perchlorate Exposure is Associated with Oxidative Stress and Indicators of Serum Iron Homeostasis Among NHANES 2005–2008 Subjects

Dina M Schreinemachers 1,✉, Andrew J Ghio 1, Jon R Sobus 2, Marc A Williams 1,*
PMCID: PMC4310500  PMID: 25673971

Abstract

Perchlorate (ClO4−), an oxidizing agent, is a ubiquitous environmental pollutant. Several studies have investigated its thyroid hormone disrupting properties. Its associations with other biological measures are largely unknown. This study, combining 2005–2008 National Health and Nutrition Examination Surveys, investigated associations between urinary perchlorate and biomarkers of iron homeostasis, lipids, blood cell counts, and glucose metabolism. Healthy males (n = 3705), non-pregnant females (n = 2967), and pregnant females (n = 356), aged 12–59 years, were included in the linear regression models, which showed significant positive (+) and negative (−) associations for both males and non-pregnant females with serum uric acid (−), serum iron (−), RBC count (−), blood urea nitrogen (+), and lymphocyte count (+). Other significant associations were observed for either males or non-pregnant females. Among pregnant females, perchlorate was significantly associated with blood urea nitrogen (+) and serum iron (−). These associations may be indicators of perchlorate’s potential effect on several biological systems, which when considered in total, may implicate perturbation of iron homeostasis.

Keywords: perchlorate, epidemiology, biomarkers, iron homeostasis

Introduction

Biomonitoring data, including those collected as part of the National Health and Nutrition Examination Survey (NHANES), can contribute insight into how xenobiotics affect biological systems by providing information both on their presence or their metabolites in blood and urine, and on levels of many biological measures. Establishing an association between internal pollutants and disturbance of biochemical and physiological processes may help establish an adverse outcome pathway for a particular pollutant or a group of pollutants.

Perchlorate (ClO4−) is a widespread environmental contaminant.1 It occurs naturally in mostly arid regions in the form of fertilizer and potash ore. Man-made sources include fireworks, rocket propellants, and explosives. Perchlorate can be found in vegetation, ground, and surface water. Human beings are exposed to it through food and water. Perchlorate is known to compete with iodide uptake2 and has been shown to be associated with thyroid hormone (TH) levels at low environmental exposures.3,4 A previous study involving NHANES 2001–2002 data observed decreased levels of hemoglobin (HGB) and high-density lipoprotein cholesterol (HDL) in association with urinary perchlorate.5 This study involving data from combined NHANES 2005–2006 and NHANES 2007–2008 (NHANES 2005–2008) is a continuation of the previous NHANES 2001–2002 study. Use of a larger dataset provides the opportunity to further investigate if exposure to perchlorate can be linked to changes in the level of biomarkers that are known or suspected to be associated with adverse health outcomes, such as cardiovascular disease, impaired glucose metabolism, and anemia. These outcomes have been associated with hypothyroidism.6–8

Materials and Methods

Data source and exclusion criteria

The NHANES are designed to assess the health and nutritional status of the civilian, noninstitutionalized US population. These surveys include household interviews on health conditions, physical examinations, and collection of blood and spot urine samples. Subjects are selected based on a complex multistage probability sampling design. Participants aged 6 years and older in the combined NHANES 2005–2006 and NHANES 2007–2008 were tested for perchlorate. Urine samples from both the surveys were frozen (−20 °C) until shipment on dry ice to the CDC Division of Environmental Health Laboratory Sciences. Urinary perchlorate was determined by ion chromatography tandem mass spectrometry. NHANES 2005–2008 participants with known urinary perchlorate levels were selected for this study. They represented 75% of the total NHANES 2005–2008 population. To avoid potential interference of previous diseases, subjects were excluded if in the past they had been diagnosed with cancer, heart failure, coronary heart disease, angina, myocardial infarction, stroke, thyroid problems, diabetes, or prediabetes, defined as having impaired glucose tolerance or elevated fasting glucose. Subjects were also excluded if they were on at least one of the several classes of medications categorized as antineoplastic, cardiovascular, hormonal, or metabolic, taken in the month prior to the interview. Using such broad criteria for exclusion of medication assured us that most subjects with relevant medical problems would be excluded from the study. In addition, underweight subjects (body mass index [BMI] <18.5), which could be an indication of nutritional deficiencies, were also excluded.

The standard biochemistry profile was available only for subjects aged ≥12 years. Pregnant and nonpregnant females were analyzed separately. Pregnancy was determined by the human chorionic gonadotropin-based urine test.

Some laboratory tests were performed only on specific groups of subjects/samples. For example, some tests were performed only on morning (fasting) samples, NHANES 2005–2006 or NHANES 2007–2008 samples, or samples from females only. Where necessary, this information is shown in the tables. Collection procedures and laboratory methods have been previously described in detail.9

Statistical analyses

Urinary perchlorate was determined for 15,326 of the 20,497 subjects in NHANES 2005–2008. Subjects aged 6–11 years (n = 2,197) were excluded from this study due to the lack of biochemistry profile data. The remaining subjects were further divided into age groups 12–59 years (n = 9,877) and ≥60 years (n = 3,252). The reason for this division is that older subjects may be more susceptible to oxidative stress and inflammation10 and may therefore react differently to xenobiotic exposures. After excluding subjects with previous diseases or on current prescriptions from age groups 12–59 years (23%, n = 2,297) and ≥60 years (83%, n = 2,705), the remaining numbers of subjects were 7,580 in the 12–59 years age group and 547 in the ≥60 years age group. The ≥60 years age group (316 males and 231 females) was considered too small for gender-specific analyses and was excluded from further study. Among 12–59 years age group, an additional number of 552 underweight subjects were excluded, resulting in 7,028 subjects, aged 12–59 years (3,705 males and 3,323 females) available for analysis.

A correction for urinary concentration was applied by dividing urinary perchlorate concentration (μg/L) by urinary creatinine concentration (mg/dL). This method is used commonly to adjust for urine volume status.11 Nonparametric Spearman correlations, plots, and linear regression analyses were used to investigate the association between the exposure variables and several biological measures as the dependent variable.12 Smoothed interpolations of the unadjusted observations were used for the plots. The regressions were based on Proc Surveyreg (SAS), which performs linear regressions on observations from non-randomly selected study populations, with adjustment for potential correlations between sampling clusters and strata. Because we were focused on population subsets, and not on developing national estimates, NHANES sample weights were not included. Not including NHANES sample weights, but including demographics in the regression as done here, is regarded as a reasonable compromise between efficiency and bias.13

The following biomarkers of interest available in NHANES 2005–2008 were tested as the dependent variables in regression analyses for their association with perchlorate: serum uric acid; blood urea nitrogen (BUN); serum iron; erythrocyte number; HGB; ferritin; total iron binding capacity; percent transferrin saturation; C-reactive protein (CRP); number and percent of neutrophils, monocytes, lymphocytes, and platelets; total cholesterol; high-density lipoprotein (HDL); non-HDL; and fasting subsample including low- density lipoprotein (LDL), apolipoprotein-B (Apo-B), triglycerides, glucose tolerance (2 hours, 75 g of glucose), glucose, and insulin. These measures were natural log-transformed in order to reduce skewness. These biological measures were selected for two reasons: (1) they are known to be associated with several interrelated biological systems and processes, including oxidative stress, kidney function, iron homeostasis, inflammation, lipid homeostasis, and glucose metabolism, and (2) their values were available in the NHANES 2005–2008 data.

Besides the biomarkers of interest and the main exposure variable, the regression models included several explanatory variables that adjusted for individual characteristics: age (continuous); race/ethnicity: non-Hispanic White (reference), Hispanic American, non-Hispanic Black, other race/ethnicity; BMI (continuous, log-transformed); serum cotinine: low (<0.015 ng/mL, referent), medium (0.015–10 ng/mL), high (>10 ng/mL); poverty index, the ratio of family income to poverty threshold (dichotomous, <1 versus ≥1); hours of fasting (log- transformed, continuous); daily kilocalorie intake (continuous, natural log-transformed); an indicator variable for the NHANES dataset with value 0 for 2005–2006 and 1 for 2007–2008.

The regression analyses produced parameter estimates β [95% confidence interval (CI), P value] representing the change of the log-transformed dependent variable associated with a unit change of the log-transformed urinary-creatinine-adjusted perchlorate [log (μg/10 mg)], with adjustment for other explanatory variables. Significant associations are defined as a CI excluding the value zero and a P value ≤0.05. To facilitate interpretation of the regression models, the percent change for selected biomarkers was calculated based on the estimate for the β, and assuming a 10-fold increase of the exposure measure, eg, from 0.01 to 0.10 urinary perchlorate (μg/L)/urinary creatinine (mg/L), holding all other variables constant, according to the following formula: (10β – 1) × 100.14

We selected not to use Bonferroni adjustments for multiple comparisons. As long as new findings are submitted to further scrutiny in order to rule out chance findings, it should not make a difference if one is investigating many associations in as many studies or combined in a single study.15,16

Results

Gender-specific demographics and biomarker levels are presented in Table 1.

Table 1.

Characteristics and biomarker levels of subjects, age 12–59, NHANES 2005–2008.

CHARACTERISTICS MALES (n = 3705) FEMALES (n = 3323a)
PERCENTAGE OR MEDIAN (RANGE) NUMBER MISSING PERCENTAGE OR MEDIAN (RANGE) NUMBER MISSING
Age
 12–19, 37 38
 20–39, 38 40
 40–59, 25 22
BMI 26.0 (18.5–71.8) 26.0 (18.5–76.1)
Ethnicity
 White% 37 32
 Hispanic american% 34 37
Nonhispanic Black% 25 26
 Other ethnicity % 4 5
 Cotinine (ng/mL) 232 234
<0.015 11 21
 0.015–10 58 62
>10 31 17
Below poverty index (%) 23 256 26 216
Daily kcal intake 2512 (149–13509) 139 1792 (89–10436) 125
Hours of fasting 6 (0.5–64) 90 5 (0.5–86) 79
Urinary perchlorate (ng/mL) 4.28 (0.18–348) 3.53 (0.12–74)
Urinary creatinine (mg/dL) 153 (6–678) 116 (5–510)
Serum uric acid (mg/dL) 5.8 (1.4–11.7) 244 4.3 (1.2–9.8) 241
Blood urea nitrogen (mg/dL) 11 (2–27) 243 9 (1–57) 241
Serum iron (refrig.) (μg/dL) 89 (15–315) 244 71 (7–382) 241
RBC (106/μL) 5.08 (3.16–7.08) 221 4.47 (2.44–6.67) 208
HGB (g/dL) 15.4 (8.8–18.8) 221 13.3 (6.2–18.3) 208
Ferritin (ng/mL) na 3705 32 (2–611) 473
TIBCb (μg/dL) na 3705 368 (224–721) 1542
Percent transferrin saturationb (%) na 3705 19.1 (1.4–95.0) 1542
CRP (< = 3) (mg/dL) 0.10 (0.01–3.00) 229 0.14 (0.01–2.99) 224
Total cholesterol (mg/dL) 179 (78–458) 230 179 (87–411) 235
HDL (mg/dL) 46 (7–128) 230 55 (15–155) 235
Non-HDL (mg/dL) 130 (24–420) 230 121 (34–314) 235
LDLc (mg/dL) 105 (19–344) 2063 101 (22–253) 1900
Apolipoprotein-Bc (mg/dL) 84 (22–318) 2013 80 (24–179) 1883
Triglyceridesc (mg/dL) 99 (14–1600) 2023 87 (18–693) 1886
WBC (103/μL) 6.9 (1.5–19.2) 221 7.3 (2.3–55.9) 208
Neutrophil number (103/μL) 3.8 (0.4–17.8) 225 4.3 (0.7–16.3) 224
Neutrophil percent 55.8 (18.3–92.7) 225 58.9 (9.1–90.4) 224
Monocyte number (103/μL) 0.6 (0.1–1.8) 225 0.5 (0.1–1.9) 224
Monocyte percent 8.1 (0.6–39.1) 225 7.2 (0.8–26.3) 224
Lymphocyte number (103/μL) 2.2 (0.5–6.6) 225 2.2 (0.6–48.4) 224
Lymphocyte percent 31.9 (5.8–64.8) 225 30.3 (7.2–86.6) 224
Platelets (103/μL) 263 (56–562) 221 287 (4–1000) 208
Glucose tolerance testc (mg/dL) 98 (33–491) 2277 100 (32–403) 2228
Fasting insulinc (μU/mL) 9.0 (1–151) 2029 10.2 (1–231) 1893
Fasting glucosec (mg/dL) 98 (63–334) 2009 93 (38–250) 1870
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Notes:

a

among the 3323 females, 356 (11%) were pregnant.

b

only available for women in NHANES 2005–2006.

c

AM samples.

Abbreviation: NA, not available.

Regression analysis for males and females aged 12–59 years, excluding pregnant women (n = 6672)

These analyses involving 3,705 males and 2,967 non-pregnant females formed the main body of this study, which investigated associations of perchlorate with the biochemistry profile biomarkers and blood counts.

Regression-based associations between creatinine-adjusted perchlorate and biomarkers are presented in Table 2. For both males and females, serum uric acid, serum iron, and red blood cell (RBC) count have a strong negative association with perchlorate, while BUN and lymphocyte number have a positive association with perchlorate. The unadjusted associations between perchlorate and these biomarkers are shown in Figures 1A–1E. At least 99% of the values for serum uric acid, BUN, and lymphocyte count were within normal range. Regarding serum iron, 16% of the males and 36% of the females were below the lower limit of the reference range (60 μg/dL). Regarding RBC counts, 8.9% of the males and 23% of the females were below their respective reference levels of 4.6×109 and 4.2×109.

Table 2.

Association of selected biomarkers with urinary perchlorate/creatinine for males and non-pregnant females, age 12–59, BMI ≥18.5, NHANES 2005–2008.

BIOMARKER (NATURAL LOG TRANSFORMED) MALES FEMALES
N ESTIMATE (95% CI) P-VALUE N ESTIMATE (95% CI) P-VALUE
Oxidative stress, kidney function, iron homeostasis
Serum uric acid (mg/dL) 3135 −0.033 (−0.044, −0.023) <0.0001 2500 −0.027 (−0.038, −0.016) <0001
Blood urea nitrogen (mg/dL) 3136 0.029 (0.011, 0.046) 0.003 2500 0.045 (0.025, 0.065) <0.0001
Serum iron (μg/dL) 3135 −0.064 (−0.085, −0.043) <−0.0001 2500 −0.053 (−0.081, −0.025) 0.0006
RBC (106/μL) 3145 −0.008 (−0.013, −0.004) 0.0005 2503 −0.006 (−0.010, −0.002) 0.006
HGB (g/dL) 3145 −0.011 (−0.015, −0.007) <0.0001 2503 −0.000 (−0.005, 0.005) 0.998
Ferritin (ng/mL) age 12–49 NA NA NA 2294 −0.022 (−0.064, 0.020) 0.287
TIBC (μg/dL)a NA NA NA 1381 0.003 (−0.008, 0.014) 0.537
Transferrin saturation (%)a NA NA NA 1381 −0.060 (−0.106, −0.013) 0.016
Lipids
total cholesterol (mg/dL) 3148 −0.011 (−0.020, −0.002) 0.022 2505 0.002 (−0.008, 0.013) 0.648
HDL (mg/dL) 3148 −0.006 (−0.017, 0.005) 0.252 2505 0.010 (−0.006, 0.026) 0.218
Nonhdl (mg/dL) 3148 −0.012 (−0.025, 0.000) 0.058 2505 −0.001 (−0.014, 0.012) 0.921
LDL (mg/dL)b 1492 −0.021 (−0.039, −0.004) 0.019 1131 0.009 (−0.010, 0.028) 0.323
Apolipoprotein-B (mg/dL)b 1533 −0.017 (−0.032, −0.001) 0.038 1140 0.004 (−0.016, 0.024) 0.676
Triglycerides (mg/dL)b (excl 9 obs >900) 1521 −0.021 (−0.049, 0.007) 0.142 1137 −0.001 (−0.046, 0.043) 0.954
CRP, WBC, platelets
CRP (excl obs >3) (mg/dL) 3128 −0.045 (−0.097, 0.006) 0.084 2491 −0.059 (−0.123, 0.005) 0.071
WBC (1000/μL) 3145 0.009 (−0.007, 0.024) 0.253 2503 0.011 (−0.003, 0.025) 0.130
Neutrophil # (1000/μL) 3143 0.003 (−0.020, 0.026) 0.793 2496 −0.000 (-0.023, 0.023) 0.994
Neutrophil (%) 3143 −0.006 (−0.015, 0.004) 0.258 2496 −0.011 (−0.023, 0.001) 0.071
Monocytes # (1000/μL) 3143 0.007 (−0.008, 0.022) 0.368 2496 0.020 (0.004, 0.036) 0.014
Monocytes (%) 3143 −0.005 (−0.017, 0.008) 0.452 2496 0.009 (−0.009, 0.028) 0.311
Lymphocytes # (1000/μL) 3143 0.019 (0.004, 0.034) 0.016 2496 0.030 (0.011, 0.048) 0.002
Lymphocytes (%) 3143 0.010 (−0.004, 0.024) 0.149 2496 0.019 (0.001, 0.037) 0.043
Platelets (1000/μL) 3145 0.014 (0.003, 0.025) 0.018 2503 −0.005 (−0.019, 0.010) 0.522
Glucose metabolism
Glucose toleranceb (mg/dL, 2 hrs) 1323 −0.004 (−0.024, 0.016) 0.706 992 −0.010 (−0.033, 0.013) 0.366
Insulin (μU/mL)b 1519 0.044 (0.003, 0.084) 0.034 1130 0.048 (−0.008, 0.104) 0.088
Glucose (mg/dL)b 1534 0.010 (0.003, 0.018) 0.006 1140 0.02 (−0.007, 0.010) 0.715
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Notes: Estimates are adjusted for age, BMI, ethnicity, cotinine, poverty index, daily kcal intake, fasting duration, NHANES survey.

a

NHANES 2005–2006 only.

b

AM sample. Significant positive (+) and negative (−) associations with creatinine-adjusted perchlorate for both males and females were observed for serum uric acid (−), serum iron (−), RBC count (−), blood urea nitrogen (+), and lymphocyte number. Additional associations among males were observed for hemoglobin (−), total cholesterol (−), LDL (−), apolipoprotein (−), platelets (+), insulin (+), glucose (+), and among females for transferrin saturation (−), monocyte number (+), lymphocyte percent (+).

Figure 1.

Figure 1

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Trends and spearman correlations between urinary perchlorate and selected biomarkers among healthy NHANES 2005–2008 males and non-pregnant females, age 12–59 years. (A) serum uric acid; (B) blood urea nitrogen; (C) serum iron; (D) red blood cells; (E) lymphocyte number. The y-axis represents the biomarker levels. The x-axis represents urinary creatinine-adjusted perchlorate levels.

The percent changes of these biomarkers for males/females based on the estimates for the betas, and assuming a 10-fold change of the exposure measure “urinary perchlorate (μg/L)/urinary creatinine (mg/L)”, were as follows: serum uric acid, −7.3/−6.0; BUN 6.9/10.9; serum iron −13.7/−11.5; RBC −1.8/−1.4; lymphocyte count 4.5/7.2.

Other significant associations were observed among males for HGB (strongly negative); total cholesterol, LDL, and apolipoprotein-B (negative); platelets, insulin, and glucose (positive); and it was observed among females for transferrin saturation (negative), monocyte number, and lymphocyte percent (positive) (Table 2).

Initial Spearman correlation analyses showed that several of the outcome measures of interest were strongly associated with serum iron, but showed only a weak or no association with perchlorate (data not shown). This was confirmed by repeating the regression analyses using serum iron as the main explanatory variable instead of perchlorate (Table 3). Examples of biomarkers that were weakly or not associated with perchlorate, but significantly associated with serum iron, are measures related to iron homeostasis for females; total cholesterol, HDL, CRP, monocytes, platelets, and insulin for males and females; and LDL, apolipoprotein and triglycerides, and lymphocyte percent for males. Taking this one step further by using HDL as the main explanatory variable in the regression analyses, it is shown that HDL, which is not associated with perchlorate, is strongly associated with apolipoprotein-B, triglycerides, CRP, WBC count, neutrophil and lymphocyte count, and the glucose metabolism measures for both males and females (Table 4). These results may reflect the different metabolic stages of the subjects. In other words, exposure to perchlorate and potentially other stressors may be associated with serum iron levels, which in turn may be associated with HDL, which in turn may be associated with other markers.

Table 3.

Association of selected biomarkers with serum iron for males and non-pregnant females, age 12–59, BMI ≥18.5, NHANES 2005–2008.

BIOMARKER (NATURAL LOG TRANSFORMED) MALES FEMALES
N ESTIMATE (95% CI) P-VALUE N ESTIMATE (95% CI) P-VALUE
Oxidative stress, kidney function, iron homeostasis
Serum uric acid (mg/dL) 3135 0.078 (0.057, 0.099) <0.0001 2500 0.051 (0.035, 0.066) <0.0001
Blood urea nitrogen (mg/dL) 3135 0.007 (−0.018, 0.032) 0.587 2500 0.038 (0.013, 0.064) 0.005
RBC (106/μL) 3130 0.011 (0.004, 0.019) 0.006 2496 0.011 (0.004, 0.018) 0.005
HGB (g/dL) 3130 0.048 (0.041, 0.055) <0.0001 2496 0.085 (0.073, 0.097) <0.0001
Ferritin (ng/mL) age 12–49 NA NA NA 2287 0.746 (0.659, 0.833) <0.0001
TIBC (μg/dL)a NA NA NA 1376 −0.058 (−0.079, −0.037) <0.0001
Transferrin saturation (%)a NA NA NA 1376 1.049 (1.023, 1.075) <0.0001
Lipids
total cholesterol (mg/dL) 3134 0.030 (0.011, 0.049) 0.003 2500 0.015 (−0.000, 0.029) 0.051
HDL (mg/dL) 3134 0.068 (0.044, 0.091) <0.0001 2500 0.044 (0.025, 0.063) <0.0001
Nonhdl (mg/dL) 3134 0.018 (−0.008, 0.044) 0.175 2500 0.003 (−0.018, 0.023) 0.796
LDL (mg/dL)b 1486 0.059 (0.032, 0.086) 0.0001 1127 −0.003 (−0.037, 0.030) 0.845
Apolipoprotein-B (mg/dL)b 1527 0.044 (0.017, 0.071) 0.003 1136 −0.004 (−0.037, 0.028) 0.793
Triglycerides (mg/dL)b (excl 9 obs >900) 1515 0.075 (0.011, 0.138) 0.023 1133 0.019 (−0.033, 0.071) 0.466
CRP, WBC, platelets
CRP (excl obs >3) (mg/dL) 3360 −0.876 (−1.361, −0.391) 0.028 2484 −0.371 (−0.485, −0.257) <0.0001
WBC (1000/μL) 3130 −0.113 (−0.135, −0.091) <0.0001 2496 −0.013 (−0.036, 0.010) 0.252
Neutrophil # (1000/μL) 3128 −0.157 (−0.188, −0.126) <0.0001 2489 −0.004 (−0.034, 0.027) 0.800
Neutrophil (%) 3128 −0.045 (−0.060, −0.029) <0.0001 2489 0.008 (−0.004, 0.021) 0.165
Monocytes # (1000/μL) 3128 −0.073 (−0.107, −0.039) 0.0001 2489 −0.047 (−0.077, −0.017) 0.003
Monocytes (%) 3128 0.040 (0.011, 0.068) 0.007 2489 −0.031 (−0.051, −0.010) 0.004
Lymphocytes # (1000/μL) 3128 −0.020 (−0.051, 0.011) 0.199 2489 −0.003 (−0.026, 0.020) 0.805
Lymphocytes (%) 3128 0.092 (0.059, 0.126) <0.0001 2489 0.009 (−0.010, 0.029) 0.339
Platelets (1000/μL) 3130 −0.041 (−0.066, −0.016) 0.002 2496 −0.068 (−0.088, −0.048) <0.0001
Glucose metabolism
Glucose toleranceb (mg/dL, 2 hrs) 1321 0.007 (−0.034, 0.049) 0.723 989 0.004 (−0.028, 0.037) 0.786
Insulin (μU/mL)b 1517 −0.110 (−0.190, −0.031) 0.008 1128 −0.105 (−0.169, −0.042) 0.002
Glucose (mg/dL)b 1527 −0.013 (−0.028, 0.001) 0.071 1134 −0.007 (−0.017, 0.002) 0.134
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Notes: Estimates are adjusted for age, BMI, ethnicity, cotinine, poverty index, daily kcal intake, fasting duration before blood sample, NHANES survey.

a

NHANES 2005–2006 only.

b

AM sample. NA: not available. Significant positive (+) and negative (−) associations with serum iron for both males and females were observed for serum uric acid (+), RBC count (+), HGB (+), total cholesterol (+), HDL (+), CRP (−), monocyte # (−), platelets (−), insulin (−). Additional associations among males were observed for LDL (+), apolipoprotein-B (+), triglycerides (+), WBC (−), neutrophil # (−), neutrophil % (−), monocyte % (+), lymphocyte % (+), and among females ferritin (+), TIBC (−), transferrin saturation (+), monocyte % (−).

Table 4.

Association of selected biomarkers with HDL for males and non-pregnant females, age 12–59, BMI ≥18.5, NHANES 2005–2008.

BIOMARKER (NATURAL LOG TRANSFORMED) MALES FEMALES
N ESTIMATE (95% CI) P-VALUE N ESTIMATE (95% CI) P-VALUE
Lipids
LDL (mg/dL)b 1492 0.015 (−0.063, 0.093) 0.700 1131 −0.070 (−0.168, 0.028) 0.154
Apolipo protein-B (mg/dL)b 1533 −0.182 (−0.235, −0.129) <0.0001 1140 −0.167 (−0.245, −0.089) 0.0001
Triglycerides (mg/dL)b (excl 9 obs >900) 1521 −0.987 (−1.107, −0.867) <0.0001 1137 −0.739 (−0.897, −0.580) <0.0001
CRP, WBC, platelets
CRP (excl obs >3) (mg/dL) 3375 −0.404 (−0.408, −0.401) 0.0004 2489 −0.427 (−0.586, −0.268) <0.0001
WBC (1000/μL) 3143 −0.111 (−0.158, −0.065) <0.0001 2501 −0.129 (−0.167, −0.091) <0.0001
Neutrophil # (1000/μL) 3141 −0.074 (−0.140, −0.008) 0.030 2494 −0.113 (−0.165, −0.061) 0.0001
Neutrophil (%) 3141 0.038 (0.012, 0.064) 0.005 2494 0.011 (−0.016, 0.037) 0. 416
Monocytes # (1000/μL) 3141 −0.020 (−0.074, 0.034) 0.452 2494 −0.068 (−0.119, −0.017) 0.010
Monocytes (%) 3141 0.089 (0.039, 0.140) 0.001 2494 0.051 (0.004, 0.099) 0.036
Lymphocytes # (1000/μL) 3141 −0.180 (−0.218, −0.142) <0.0001 2494 −0.137 (−0.182, −0.092) <0.0001
Lymphocytes (%) 3141 −0.069 (−0.114, −0.024) 0.004 2494 −0.013 (−0.060, 0.033) 0.556
Platelets (1000/μL) 2143 0.013 (−0.018, 0.044) 0.407 2501 −0.044 (−0.085, −0.002) 0.040
Glucose metabolism
Glucose toleranceb (mg/dL, 2 hrs) 1322 −0.142 (−0.206, −0.078) <0.0001 992 −0.119 (−0.202, −0.036) 0.007
Insulin (μU/mL)b 1518 −0.645 (−0.794, −0.496) <0.0001 1130 −0.479 (−0.630, −0.328) <0.0001
Glucose (mg/dL)b 1532 −0.041 (−0.076, −0.005) 0.026 1138 −0.047 (−0.071, −0.024) 0.0003
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Notes: Estimates are adjusted for age, BMI, ethnicity, cotinine, poverty index, daily kcal intake, fasting duration before blood sample, NHANES survey.

b

AM sample. Significant positive (+) and negative (−) associations with HDL for both males and females were observed for apolipoprotein-B (−), triglycerides (−), CRP (−), WBC count (−), neutrophil count (−), monocyte % (+), lymphocyte count (−), glucose tolerance (−), insulin (−), glucose (−). additional associations among males were observed for neutrophil %(+), lymphocyte percent (−), and among females for monocyte count (−), platelets (−).

Pregnant women (n = 356)

Due to the small sample size, restricted analyses were performed for the pregnant females. Only those biomarkers that had been shown to be significantly associated with perchlorate among both males and non-pregnant females (serum uric acid, BUN, serum iron, RBC, and number of lymphocytes) were analyzed. Among the 305 pregnant women with non-missing regression covariates, significant associations were observed for BUN (β = 0.063 [0.000, 0.126], P = 0.0497) and serum iron (β = −0.063 [−0.122, −0.005], P = 0.035). These estimates are compatible with those observed for the non-pregnant females (Table 2). No associations were observed for serum uric acid, RBC, or lymphocyte number (data not shown).

Dietary supplements among 12–59-year-old males and nonpregnant females, and among pregnant females

To assess whether intake of dietary supplements during 30 days prior to the interview influenced the association between perchlorate and serum iron, a term for dietary supplement intake (yes/no) was included in the regression model for serum iron. Supplements were used by 26% of the males, 30% of the non-pregnant females, and 76% of the pregnant females. Among the males (n = 3135), the use of supplements was not significantly associated with serum iron: β = 0.009, 95% CI (−0.031, 0.049), P = 0.66. Among the non-pregnant females (n = 2499), supplement intake was borderline significantly associated with serum iron: β = 0.044, 95% CI (−0.002, 0.091), P = 0.06. Among the 305 pregnant females, the association was significantly positively associated: β = 0.128, 95% CI (0.005, 0.250), P = 0.042. However, the use of dietary supplement had no effect on the negative association between serum iron and urinary perchlorate among pregnant women.

Association with THs among 12–59-year-old males and nonpregnant females

A previous study observed significant associations between urinary perchlorate and THs among NHANES 2007–2008 subjects.4 Although the purpose of our study was to investigate associations between perchlorate and biomarkers other than THs, it was of interest to determine if the biomarkers, such as serum uric acid, BUN, serum iron, RBC count, and lymphocyte count, correlated with THs, thyroid peroxidase antibodies, and thyroglobulin among the NHANES 2007–2008 healthy males (n = 1603) and non-pregnant females (n = 1255). Spearman correlations (r) with P < 0.0001(**) or <0.01 (*) are presented here. Serum uric acid was correlated with TSH (males, r = 0.07*; females, r = 0.09*). BUN was correlated with thyroglobulin (males, r = −0.11**), TSH (females, r = 0.10*), and T3 (males, r = −0.11**; females, r = −0.15**). Serum iron was correlated with thyroid peroxidase antibodies (males, r = 0.09*) and TSH (males, r = −0.07*). RBC count was correlated with T4 (males, r = 0.12**; females, r = 0.13**) and T3 (males, r = 0.07*; females, r = 0.14**). Lymphocyte count was correlated with TSH (males, r = 0.12**; females, r = 0.09*), T4 (males, r = 0.09*), and T3 (males, r = 0.14**; females, r = 0.11*).

Discussion

Although the TH disrupting characteristics of perchlorate have been studied, less is known about effects on other human biological processes. Perchlorate has a short half-life (6–8 hours) and is eliminated from the body unchanged. The perchlorate anion, recognized as an oxidizing agent, has limited reactivity due to strong chlorine–oxygen bonds. Degradation by chemical reduction is therefore not very successful, although microbial degradation under anaerobic conditions can reduce perchlorate to chloride.1 Given perchlorate’s short half-life and low reactivity, it is not immediately obvious how to interpret the observed associations with biomarkers such as levels of RBC counts, serum iron, uric acid, BUN levels, and lymphocyte count in both genders. Comparison of the regressions that use perchlorate (Table 2) and serum iron (Table 3) as the main explanatory variable shows that serum iron is associated with several biological measures in contrast to perchlorate with fewer associations. We may be dealing with a stepwise process, where serum iron levels represent the combined influences from several exposures including perchlorate. Vineis et al.17 explained that the traditional toxicology model of studying single agents, ie, one at a time, at different doses, does not work any longer when dealing with multiple, environmental exposures. Therefore, “acquired vulnerability” should be considered, which represents the cumulative effects contributed by different exposures, which can lead to increased biological changes and predispose to disease. The problem is finding a biomarker that may represent acquired vulnerability. If we are correct in assuming that the level of serum iron is affected by multiple exposures that perturb iron homeostasis, then serum iron represents the endpoint of acquired vulnerability of an exposed subject and perchlorate is a contributor to this endpoint.

Because serum iron is an important health indicator, studying xenobiotics that may potentially affect its levels is important. Serum iron as a measure of iron availability is recognized to impact RBC counts, with greater metal concentrations increasing cell numbers and numerous erythrocytic indices.18 Accordingly, perchlorate and its association with decreased serum iron in our study (Table 2) could affect changes in the RBC counts (Table 3). Similarly, concentrations of uric acid correlate with iron. Such correlations have been previously demonstrated between serum urate and indices of total body iron.19 The only enzymatic source of urate is xanthine oxidoreductase. In cultured cells, the activity and expression of xanthine oxidoreductase have been demonstrated to increase after exposures to iron.20 In further support of a possible association between levels of iron and urate, exposures of both rodents21,22 and humans23 to elevated concentrations of this metal increase urate. Concordantly, uric acid decreased with exposures to perchlorate (Table 2) and diminished iron availability (Table 3). Te association between perchlorate exposure and BUN among females can reflect a potential biological effect of the metal on renal function (Table 3).24 Other significant correlations between perchlorate exposure and biomarkers could result from an association of the endpoint with indices of iron homeostasis. This includes the relationships between perchlorate and indices of lipids, glucose metabolism, and populations of white blood cells in males or females.25–32

In the human, dietary iron absorption requires that iron traverse both the apical and basolateral membranes of the intestinal epithelial cells to reach the blood where it is incorporated into and transported by transferrin.33 Te transport of iron across membranes occurs in the ferrous (Fe2+) state via the divalent metal transporter 1, the major intestinal iron importer. However, dietary iron exists mainly in ferric form (Fe3+) and subsequently must be reduced to the ferrous state prior to its uptake. The protein duodenal cytochrome B, participates in the chemical reduction of iron (ie, ferrireduction) for intracellular uptake, and is localized on the apical membrane of intestinal enterocytes. Perchlorate potentially diminishes iron uptake in the human intestine following either a direct interaction of perchlorate with iron or an indirect depletion of systemic reductants. There are at least two possible direct interactions with perchlorate. First, perchlorate might complex iron in the gastrointestinal tract, producing iron III and iron II perchlorates, Fe3+(ClO4−)3 and Fe2+(ClO4−)2, respectively. Such metal complexes would decrease the capacity of iron to support electron transport and therefore preclude uptake of the metal in a reduced valence state by the intestinal epithelium. Such perchlorate and iron complexes have been proposed as a methodology by which ClO4− can be removed from potable water.1 Second, perchlorate has some capacity to directly oxidize Fe2+ to Fe3+.34 This would decrease uptake of the metal in the intestine since iron in the reduced valence state would be diminished, and it is the Fe2+ which is transported across the membrane.

Alternatively, human iron homeostasis may be disrupted by perchlorate in a manner comparable to an acute phase reaction albeit without obvious evidence of inflammation, eg, an elevated C-reactive protein (Table 2). In an acute phase reaction, there is a response to an exposure (eg, infectious agent), which prompts the host to decrease availability of iron by impacting the hepcidin–ferroportin interaction. There is an isolation of host iron in the reticulo-endothelial system and serum iron subsequently decreases. Such a response to an exposure is postulated to be beneficial in that iron is not available for the infectious agent. Infections are absolutely dependent on an availability of host iron and the isolation of this metal in the reticulo-endothelial system would be beneficial in that proliferation of the microbe would be controlled. It can be proposed that such a pathway might also be functioning with perchlorate. Comparable to microbes, perchlorate has a capacity to bind iron and diminish concentrations available to the host. The host may conclude that this is a threat/challenge and initiate the same pathway.35

An additional interpretation is based on the capacity of the perchlorate anion to have a high binding affinity for transferrin combined with the ability to displace iron from this transport protein.36 These characteristics may contribute to the observed decrease of serum iron levels among both males and females and the decrease of transferrin saturation among females (Table 2). Transferrin saturation values for males were not available. Most importantly, iron may become a poor ligand in this process. It is known that reactive oxygen species naturally occurring in the human body can produce the highly reactive hydroxyl radical in the presence of poorly bound iron. The hydroxyl radical has been implicated in tissue damage and organ failure.37

Regardless of the mechanistic pathway, interactions between perchlorate and iron could assist in understanding the observed associations of this pollutant with the end points measured in this investigation. Perchlorate diminishes iron levels and availability, which will successively impact numerous other indices that are metal dependent. Concentrations of urate, cholesterol, and HGB are included among such end points.

Associations between the perchlorate-related biomarkers and THs in this study were modest, but significant. These associations are supported by the published literature. The relationship between iron status and THs may be bidirectional.38 Iron deficiency has been reported to impair thyroid metabolism by potentially lowering heme-dependent thyroid peroxidase activity.39 Subclinical hypothyroidism is associated with impaired iron utilization and/or transport. Erdogan et al.40 reported that among all organ systems, the hematopoietic system is the most seriously affected.

The number of pregnant women available for analyses in this NHANES dataset was small. However, given the concern regarding adverse fetal development due to environmental exposures, we decided to include our limited observations. In common with the 12–59-year-old males and non-pregnant females, increased BUN and decreased serum iron levels were observed in association with perchlorate among the pregnant females. The decreased serum iron levels are of concern. It has been reported that maternal suboptimal iron stores may adversely affect fetal iron levels.41 A rat study showed that iron deficiency can cause hypothyroxinemia in pregnant rats.42 Another rat study reported that fetal/neonatal iron and TH deficiencies are associated with similar brain developmental abnormalities.43 Again, the biological effects of perchlorate may reflect an impact on iron homeostasis.

Conclusion

In general, studies involving environmental chemicals use changes in TH levels (ie, TH function) as a measure of suspected TH disruption. However, other changes in association with xenobiotics are possible without changes in TH levels, eg, changes in TH signaling (ie, TH action).44 Although previous studies investigated changes in TH function (TH levels), the current study shows that perchlorate is associated with several other biomarkers besides THs. The results indicate how NHANES biomonitoring data can identify associations between a surrogate of perchlorate exposure, ie, urinary perchlorate, and several biochemical changes. These associations can serve as hypotheses for controlled in vitro/in vivo studies investigating if exposure to environmental perchlorate is indeed the cause of oxidative stress and perturbation of iron homeostasis. Examples would include studies using exposures of relevant cell types to perchlorate with measurement of oxidant generation and indices of iron homeostasis such as iron import/export, ferritin concentration, and RNA for proteins participating in metal transport.

Acknowledgments

This study was conducted as part of the US EPA-ORD Chemical Safety for Sustainability (CSS) Research Program, Tasks CSS 3.1.2 (Use of biomarker data to develop, evaluate, and utilize integrated systems models to support risk assessment and management decisions) and CSS 3.2.1 (Develop approaches to improve biomarker research in risk assessment and management processes).

We are grateful to Wayne Cascio for his advice and recommendations. We thank Tim Wade, Ronald Hines, Michael Madden, Rob Dewoskin, and Jane Caldwell for their review and valuable comments. We thank the four Biomarker Insights reviewers for giving their time and providing useful feedback.

Glossary

Abbreviations

Apo-B

apolipoprotein-B

BMI

body mass index

BUN

blood urea nitrogen

CI

confidence interval

CRP

C-reactive protein

HDL

high-density lipoprotein

LDL

low-density lipoprotein

NHANES

National Health and Nutrition Examination Survey

RBC

red blood cells

T3

triiodothyronine

T4

thyroxine

TH

thyroid hormones

TSH

thyroid stimulating hormone

WBC

white blood cells

Footnotes

Author Contributions

Conceived and designed the experiments: DS, AG, JS, MW. Analyzed the data: DS. Wrote the first draft of the manuscript: DS. Contributed to the writing of the manuscript: DS, AG, JS. Agree with manuscript results and conclusions: DS, AG, JS, MW. Jointly developed the structure and arguments for the paper: DS, AG, JS, MW. Made critical revisions and approved final version: AG. All authors reviewed and approved of the final manuscript.

ACADEMIC EDITOR: Karen Pulford, Editor in Chief

FUNDING: Authors disclose no funding sources.

COMPETING INTERESTS: DS, AG, and JS disclose no competing interests. MW is a member of the editorial board for Biomarker Insights.

DISCLAIMER: The research described in this article has been reviewed in accordance with the US. Environmental Protection Agency policy and approved for publication. Approval does not signify that the contents necessarily reflect the views and policies of the Agency.

Human Subjects Disclaimer

Publicly available NHANES data were the source for this study; thus, requirements for informed consent have been met.

Paper subject to independent expert blind peer review by minimum of two reviewers. All editorial decisions made by independent academic editor. Upon submission manuscript was subject to anti-plagiarism scanning. Prior to publication all authors have given signed confirmation of agreement to article publication and compliance with all applicable ethical and legal requirements, including the accuracy of author and contributor information, disclosure of competing interests and funding sources, compliance with ethical requirements relating to human and animal study participants, and compliance with any copyright requirements of third parties. This journal is a member of the Committee on Publication Ethics (COPE).

REFERENCES

  • 1.Srinivasan A, Viraraghavan T. Perclorate: health effects and technologices for its removal from water resources. Int J Environ Res Public Health. 2009;6:1418–42. doi: 10.3390/ijerph6041418. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Greer MA, Goodman G, Pleus RC, Greer SE. Health effects assessment for environmental perchlorate contamination: the dose response for inhibition of thyroidal radioiodine uptake in humans. Environ Health Perspect. 2002;110:927–37. doi: 10.1289/ehp.02110927. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Blount BC, Pirkle JL, Osterloh JD, Valentin-Blasini L, Caldwell KL. Urinary perchlorate and thyroid hormone levels in adolescent and adult men and women in the United States. Environ Health Perspect. 2006;114:1865–71. doi: 10.1289/ehp.9466. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Mendez W, Jr, Eftim SE. Biomarkers of perchlorate exposure are correlated with circulating thyroid hormone levels in the 2007–2008 NHANES. Environ Res. 2012;118:137–44. doi: 10.1016/j.envres.2012.05.010. [DOI] [PubMed] [Google Scholar]
  • 5.Schreinemachers DM. Association between perchlorate and indirect indicators of thyroid dysfunction in NHANES 2001–2002 a cross-sectional, hypothesis-generating study. Biomark Insights. 2011;6:135–44. doi: 10.4137/BMI.S7985. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Duntas LH, Wartofsky L. Cardiovascular risk and subclinical hypothyroidism: focus on lipids and new emerging risk factors. What is the evidence? Thyroid. 2007;17(11):1075–84. doi: 10.1089/thy.2007.0116. [DOI] [PubMed] [Google Scholar]
  • 7.Jafarzadeh A, Poorgholami M, Izadi N, Nemati M, Rezayati M. Immunological and hematological changes in patients with hyperthyroidism or hypothyroidism. Clin Invest Med. 2010;33(5):E271–9. doi: 10.25011/cim.v33i5.14352. [DOI] [PubMed] [Google Scholar]
  • 8.Liu JJ, Brent GA. Thyroid hormone crosstalk with nuclear receptor signaling in metabolic regulation. Trends Endocrinol Metab. 2010;21(3):166–73. doi: 10.1016/j.tem.2009.11.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Centers for Disease Control and Prevention (CDC) National Center for Health Statistics (NCHS) National Health and Nutrition Examination Survey . NHANES 2005–2006, 2007–2008. Hyattsville, MD: U.S. Department of Health and Human Services; 2014. Available from: http://wwwn.cdc.gov/nchs/nhanes/search/nhanes05_06.aspx and http://wwwn.cdc.gov/nchs/nhanes/search/nhanes07_08.aspx. Laboratory protocol: http://www.cdc.gov/nchs/nhanes/nhanes2005-2006/lab_methods_05_06.htm and http://www.cdc.gov/nchs/nhanes/nhanes2007-2008/lab_methods_07_08.htm. [Google Scholar]
  • 10.Mastaloudis A, Wood SM. Age-related changes in cellular protection, purification, and inflammation-related gene expression: role of dietary phytonutrients. Ann N Y Acad Sci. 2012;1259:112–20. doi: 10.1111/j.1749-6632.2012.06610.x. [DOI] [PubMed] [Google Scholar]
  • 11.Boeniger MF, Lowry LK, Rosenberg J. Interpretation of urine results used to assess chemical exposure with emphasis on creatinine adjustments: a review. Am Ind Hyg Assoc J. 1993;54:615–27. doi: 10.1080/15298669391355134. [DOI] [PubMed] [Google Scholar]
  • 12.SAS . SAS/STAT Guide for Personal Computers, Version 9.3. Cary, NC: SAS Institute, Inc; 2010. [Google Scholar]
  • 13.Korn EL, Graubard BI. Epidemiologic studies utilizing surveys: accounting for the sampling design. Am J Public Health. 1991;81(9):1166–73. doi: 10.2105/ajph.81.9.1166. [DOI] [PMC free article] [PubMed] [Google Scholar]; Liu Y, Brent GA. Thyroid hormone crosstalk with nuclear receptor signaling in metabolic regulation. Trends Endocrinol Metab. 2010;21(3):166–73. doi: 10.1016/j.tem.2009.11.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.IDRE . FAQ: How do I Interpret a Regression Model When Some Variables are Log Transformed? Institute for Digital Research and Education; UCLA: 2014. [Accessed June 20, 2014]. Available from: http://www.ats.ucla.edu/stat/mult_pkg/faq/general/log_transformed_regression.htm. [Google Scholar]
  • 15.Rothman KJ. No adjustments are needed for multiple-comparisons. Epidemiology. 1990;1:43–7. [PubMed] [Google Scholar]
  • 16.Goldberg M, Silbergeld E. On multiple comparisons and on the design and interpretation of epidemiological studies of many associations. Environ Res. 2011;111:1007–9. doi: 10.1016/j.envres.2011.08.010. [DOI] [PubMed] [Google Scholar]
  • 17.Vineis P, Khan AE, Vlaanderen J, Vermeulen R. The impact of new research technologies on our understanding of environmental causes of disease: the concept of clinical vulnerability. Environ Health. 2009;8:54. doi: 10.1186/1476-069X-8-54. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Brugnara C. Iron deficiency and erythropoiesis: new diagnostic approaches. Clin Chem. 2003;49:1573–8. doi: 10.1373/49.10.1573. [DOI] [PubMed] [Google Scholar]
  • 19.Adamson JW. Regulation of red blood cell production. Am J Med. 1996;101:4S–6. doi: 10.1016/s0002-9343(96)00160-x. [DOI] [PubMed] [Google Scholar]
  • 20.Ghio AJ, Ford ES, Kennedy TP, Hoidal JR. The association between serum ferritin and uric acid in humans. Free Radic Res. 2005;39(3):337–42. doi: 10.1080/10715760400026088. [DOI] [PubMed] [Google Scholar]
  • 21.Stonehuerner J, Ghio AJ, Kennedy TP, Skinner KA, Skinner HB, Parks DA. Elevations in xanthine oxidase (XO) activity after silica exposure corresponds to complexed iron concentrations. Am J Respir Crit Care Med. 1998;157:A232. [Google Scholar]
  • 22.Muntane J, Puig-Parellada P, Fernandez Y, Mitjavila S, Mitjavila MT. Antioxidant defenses and its modulation by iron in acrageen-induced inflammation in rats. Clin Chim Acta. 1993;214:185–93. doi: 10.1016/0009-8981(93)90110-p. [DOI] [PubMed] [Google Scholar]
  • 23.Ward VL, McGinty JF, Church WH. Iron (III) chloride injection increases nigral uric acid in guinea-pig. Neuroreport. 1993;4:787–90. doi: 10.1097/00001756-199306000-00048. [DOI] [PubMed] [Google Scholar]
  • 24.Martines AMF, Masereeuw R, Tjalsma JG, Hoenderop JG, Wetzels JFM, Swinkels DW. Iron metabolism in the pathogenesis of iron-induced kidney injury. Nat Rev Nephrol. 2013;9:385–98. doi: 10.1038/nrneph.2013.98. [DOI] [PubMed] [Google Scholar]
  • 25.Livrea MA, Tesoriere L, Pintaudi AM, et al. Oxidative stress and antioxidant status in beta-thalassemia major: iron overload and depletion of lipid-soluble antioxidants. Blood. 1996;88:3608–14. [PubMed] [Google Scholar]
  • 26.Van Jaarsveld H, Pool GF, Barnard HC. Influence of ferritin levels on LDL cholesterol concentration in women. Res Commun Mol Pathol Pharmacol. 1997;98:201–8. [PubMed] [Google Scholar]
  • 27.Tuomainen TP, Diczfalusy U, Kaikkonen J, Nyyssonen K, Salonen JT. Serum ferritin concentration is associated with plasma levels of cholesterol oxidation products in men. Free Radic Biol Med. 2003;35(8):922–8. doi: 10.1016/s0891-5849(03)00433-7. [DOI] [PubMed] [Google Scholar]
  • 28.Fernández-Real JM, López-Bermejo A, Ricart W. Cross-talk between iron metabolism and diabetes. Diabetes. 2002;51:2348–54. doi: 10.2337/diabetes.51.8.2348. [DOI] [PubMed] [Google Scholar]
  • 29.Montonen J, Boeing H, Steffen A, et al. Body iron stores and risk of type 2 diabetes: results from the European prospective investigation into cancer and nutrition (EPIC) – Potsdam study. Diabetologia. 2012;55:2613–21. doi: 10.1007/s00125-012-2633-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Berrak SG, Angaji M, Turkkan E, Canpolat C, Timur C, Eksioglu-Demiralp E. The effects of iron deficiency on neutrophil/monocyte apoptosis in children. Cell Prolif. 2007;40:741–54. doi: 10.1111/j.1365-2184.2007.00460.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Kemp JD. The role of iron and iron binding proteins in lymphocyte physiology and pathology. J Clin Immunol. 1993;13:81–92. doi: 10.1007/BF00919264. [DOI] [PubMed] [Google Scholar]
  • 32.Seligman PA, Kovar J, Gelfand EW. Lymphocyte proliferation is controlled by both iron availability and regulation of iron uptake pathways. Pathobiology. 1992;60:19–26. doi: 10.1159/000163692. [DOI] [PubMed] [Google Scholar]
  • 33.Zhang AS, Enns CA. Molecular mechanisms of normal iron homeostasis. Hematology Am Soc Hematol Educ Program. 2009;1:207–214. doi: 10.1182/asheducation-2009.1.207. [DOI] [PMC free article] [PubMed] [Google Scholar]; Zimmermann MB, Köhrle J. The impact of iron and selenium deficiencies on iodine and thyroid metabolism: biochemistry and relevance to public health. Thyroid. 2002;12(10):867–78. doi: 10.1089/105072502761016494. [DOI] [PubMed] [Google Scholar]
  • 34.Moore AM, De Leon CH, Young TM. Rate and extent of aqueous perchlorate removal by iron surfaces. Environ Sci Technol. 2003;37:3189–98. doi: 10.1021/es026007t. [DOI] [PubMed] [Google Scholar]
  • 35.Darshan D, Anderson GJ. Interacting signals in the control of hepcidin expression. Biometals. 2009;22:77–87. doi: 10.1007/s10534-008-9187-y. [DOI] [PubMed] [Google Scholar]
  • 36.Harris WR. Anion binding properties of the transferrins. Implications for function. Biochim Biophys Acta. 2012;1820:348–61. doi: 10.1016/j.bbagen.2011.07.017. [DOI] [PubMed] [Google Scholar]
  • 37.Kell DB. Iron behaving badly: inappropriate iron chelation as a major contributor to the aetiology of vascular and other progressive inflammatory and degenerative diseases. BMC Med Genomics. 2009;2:2. doi: 10.1186/1755-8794-2-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Bremner AP, Feddema P, Joske DJ, et al. Significant associations between thyroid hormones and erythrocyte indices in euthyroid subjects. Clin Endocrin. 2012;76:304–11. doi: 10.1111/j.1365-2265.2011.04228.x. [DOI] [PubMed] [Google Scholar]
  • 39.Zimmerman MB, Köhrle J. The impact of iron and selenium deficiencies on iodine and thyroid metabolism: biochemistry and relevance to public Health. Thyroid. 2002;12(10):867–78. doi: 10.1089/105072502761016494. [DOI] [PubMed] [Google Scholar]
  • 40.Erdogan M, Kosenli A, Sencer G, Kulaksizoglu M. Characteristics of anemia in subclinical and overt hypothyroid patients. Endocr J. 2012;59(3):213–20. doi: 10.1507/endocrj.ej11-0096. [DOI] [PubMed] [Google Scholar]
  • 41.Cao C, O’Brien KO. Pregnancy and iron homeostasis: an update. Nutr Rev. 2013;71(1):35–51. doi: 10.1111/j.1753-4887.2012.00550.x. [DOI] [PubMed] [Google Scholar]
  • 42.Hu X, Teng X, Zheng H, et al. Iron deficiency without anemia causes maternal hypothyroxinemia in pregnant rats. Nutr Res. 2014;34:604–12. doi: 10.1016/j.nutres.2014.06.007. [DOI] [PubMed] [Google Scholar]
  • 43.Bastian TW, Prohaska JR, Georgieff MK, Anderson GW. Fetal and neonatal iron deficiency exacerbates mild thyroid hormone insufficiency effects on male thyroid hormone levels and brain thyroid hormone-responsive gene expression. Thyroid. 2014;155(3):1157–67. doi: 10.1210/en.2013-1571. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Zoeller TR. Environmental chemicals targeting thyroid. Hormones. 2010;9(1):28–40. doi: 10.14310/horm.2002.1250. [DOI] [PubMed] [Google Scholar]

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